Dynamic configuration and population of digital interfaces during programmatically established chatbot sessions

ABSTRACT

The disclosed exemplary embodiments include computer-implemented systems, apparatuses, and processes that dynamically configure and populate a digital interface based on sequential elements of message data exchanged during a chatbot session established programmatically between an apparatus and a device. For example, the apparatus may generate first messaging data that includes a candidate input value for an interface element of a digital interface, and transmit the first messaging data to the device during the programmatically established chatbot session. The apparatus may also receive, from the device during the programmatically established chatbot session, second messaging data that includes a confirmation of the candidate input value. Based on the second messaging data, the apparatus may generate populated interface data that associates the interface element with the confirmed candidate input value, and store the populated interface data within a memory.

TECHNICAL FIELD

The disclosed embodiments generally relate to computer-implementedsystems and processes that dynamically configure and populate digitalinterfaces during programmatically established chatbot sessions.

BACKGROUND

Many financial institutions, online retailers, and other businesses usechatbots to increase and improve a level of customer engagement betweencustomers and corresponding digital platforms such as, but not limitedto, websites, messaging applications, and mobile applications. Theseexisting chatbots may receive a message from a customer’s device (e.g.,provided as input to a corresponding digital interface),programmatically generate responses to the received message, andgenerate and transmit, to the customer’s device, a response to thereceived message for presentation within the digital interface.

SUMMARY

In some examples, an apparatus includes a communications interface, amemory storing instructions, and at least one processor coupled to thecommunications interface and to the memory. The at least one processoris configured to execute the instructions to generate first messagingdata that includes a candidate input value for a first interface elementof a digital interface, and transmit the first messaging data to adevice via the communications interface. The first messaging data istransmitted during a communications session established with anapplication program executed by the device. The at least one processoris further configured to execute the instructions to receive, via thecommunications interface, second messaging data from the device duringthe established communications session. The second messaging dataincludes a confirmation of the candidate input value, and the secondmessage data is generated by the executed application program. Based onthe second messaging data, the at least one processor is furtherconfigured to execute the instructions to generate first populatedinterface data that associates the first interface element with theconfirmed candidate input value, and store the populated interface datawithin a portion of the memory.

In other examples, a computer-implemented method includes, using atleast one processor, generating first messaging data that includes acandidate input value for a first interface element of a digitalinterface, and transmitting the first messaging data to a device via thecommunications interface. The first messaging data is transmitted duringa communications session established with an application programexecuted by the device. The computer-implemented method also includesreceiving, using the at least one processor, second messaging data fromthe device during the established communications session. The secondmessaging data includes a confirmation of the candidate input value, andthe second message data is generated by the executed applicationprogram. Based on the second messaging data, the computer-implementedmethod includes generating, using the at least one processor, firstpopulated interface data that associates the first interface elementwith the confirmed candidate input value, and storing, using the atleast one processor, the populated interface data within a portion of adata repository.

Further, in some examples, a tangible, non-transitory computer-readablemedium stores instructions that, when executed by at least oneprocessor, cause the at least one processor to perform a method thatincludes generating first messaging data that includes a candidate inputvalue for a first interface element of a digital interface, andtransmitting the first messaging data to a device via the communicationsinterface. The first messaging data is transmitted during acommunications session established with an application program executedby the device. The method also includes receiving second messaging datafrom the device during the established communications session. Thesecond messaging data includes a confirmation of the candidate inputvalue, and the second message data is generated by the executedapplication program. Based on the second messaging data, the methodincludes generating first populated interface data that associates thefirst interface element with the confirmed candidate input value, andstoring the populated interface data within a portion of a datarepository.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as claimed. Further, theaccompanying drawings, which are incorporated in and constitute a partof this specification, illustrate aspects of the present disclosure andtogether with the description, serve to explain principles of thedisclosed embodiments as set forth in the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary computing environment, inaccordance with some embodiments.

FIGS. 2A and 2B are diagrams illustrating portions of an exemplarygraphical user interface, in accordance with some embodiments.

FIGS. 3 and 4A are diagrams illustrating portions of an exemplarycomputing environment, in accordance with some embodiments.

FIG. 4B is a diagram illustrating a portion of an exemplary graphicaluser interface, in accordance with some embodiments.

FIG. 4C is a diagram illustrating a portion of an exemplary computingenvironment, in accordance with some embodiments.

FIG. 4D is a diagram illustrating a portion of an exemplary graphicaluser interface, in accordance with some embodiments.

FIG. 4E is a diagram illustrating a portion of an exemplary computingenvironment, in accordance with some embodiments.

FIG. 5 is a diagram illustrating a portion of an exemplary graphicaluser interface, in accordance with some embodiments.

FIG. 6 is a flowchart of an exemplary process for dynamicallyconfiguring and populating a digital interface during a programmaticallyestablished chatbot session, in accordance with some embodiments.

DETAILED DESCRIPTION

This specification relates to computer-implemented processes that, amongother things, dynamically configure and populate a digital interfacebased on sequential elements of message data exchanged during a chatbotsession established programmatically between a network-connectedcomputing system and a participating device operating within a computingenvironment.

By way of example, and during the programmatically established chatbotsession, the computing system may detect a request to access the digitalinterface at the participating device based on one or more elements ofthe exchanged message data. In some instances, the requested digitalinterface may include interface elements that extend across multipledisplay screens or windows when rendered for presentation at theparticipating device. Based on the detected request, the computingsystem may access locally maintained interface data that characterizesthe requested digital interface, which may include, but is not limitedto, layout data specifying a sequential disposition of each of theinterface elements across the multiple display screens, and metadatathat specifies an appropriate type or format of input data associatedwith each of the interface elements (e.g., a numerical value, anexpected range of values, etc.).

In some exemplary embodiments, described herein, the computing systemmay perform operations that dynamically predict a candidate valuerepresenting a likely input to a first one of the sequentially disposedinterface elements (e.g., a “first” interface element), and generate anadditional element of message data that provisions the candidate inputvalue to a chatbot interface generated by the participating device,e.g., a digital interface presented on a display unit of theparticipating device during the programmatically established chatbotsession. Based on additional input provided to the chatbot interface,the participating device may generate and transmit additional messagedata to the computing system that includes a confirmation of, or amodification to, candidate input value, and the computing system mayperform operations that generate an element of populated interface datafor the first interface element that includes the confirmed or modifiedinput value, e.g., that “populates” the corresponding interface elementwithin the specified input value or the now-confirmed candidate value.

Through a sequential application of these exemplary processes to each ofthe sequentially disposed interface elements within the requesteddigital interface, the computing system may populate fully the requesteddigital interface without requiring the rendering and presentation ofthe interface elements by the participating system, and based on furthermessage data transmitted through the chatbot session, may initiate aperformance of additional operations associated with the populatedinterface data. Certain of the exemplary processes described herein,which generate the elements of populated interface data and performadditional operations associated with the populated interface data basedon message data exchanged during a programmatically established chatbotsession, may be implemented in addition to, or as an alternate to,certain processes that transmit the interface elements to theparticipating device for rendering and presentation within the digitalinterface. As such, these exemplary processes, as described herein, mayenhance an ability of a user to interact with these complex digitalinterfaces through devices having display units or input units oflimited functionality, such as smart phones, wearable devices, anddigital assistants.

I. Exemplary Computing Environments

FIG. 1 is a diagram illustrating an exemplary computing environment 100that includes a computing system 130 and a client device 102, each ofwhich are operatively connected to communications network 120. Examplesof network 120 include, but are not limited to, a wireless local areanetwork (LAN), e.g., a “Wi-Fi” network, a network utilizingradio-frequency (RF) communication protocols, a Near Field Communication(NFC) network, a wireless Metropolitan Area Network (MAN) connectingmultiple wireless LANs, and a wide area network (WAN), e.g., theInternet. Although not shown, computing environment 100 may includeadditional devices, such as one or more additional client devices 102,and additional network-connected computing systems, such as one or morecomputing systems that store elements of confidential data on behalf ofcorresponding users.

Client device 102 may include a computing device having one or moretangible, non-transitory memories that store data and/or softwareinstructions, such as memory 105 that stores application repository 106.Examples of these software instructions may include, but are not limitedto, one or more application programs, application modules, and otherelements of executable code. Client device 102 may also include one ormore processors, such as processor 104, configured to execute thesoftware instructions to perform any of the exemplary processesdescribed herein.

As illustrated in FIG. 1 , client device 102 may maintain, withinapplication repository 106, an executable chatbot application 108.Chatbot application 108 may, for example, be associated with a financialinstitution, a governmental or regulatory entity, or another businessentity, such as a retailer. Further, chatbot application 108 may beprovisioned to client device 102 by computing system 130, and uponexecution by processor 104, may perform any of the exemplary processesdescribed herein to establish and maintain a programmatic communicationssession with an application program executed by computing system 130(e.g., a chatbot session programmatically established and maintainedwith a chatbot associated with a financial institution). Applicationrepository 106 may also include additional executable applications, suchas one or more executable web browsers (e.g., Google Chrome™), forexample. The disclosed embodiments, however, are not limited to theseexemplary application programs, and in other examples, applicationrepository 106 may include any additional or alternate applicationprograms, application modules, or other elements of code executable byclient device 102.

Client device 102 may also establish and maintain, within memory 105,one or more structured or unstructured data repositories or databases,such as data repository 110 that includes device data 112 andapplication data 114. In some instances, device data 112 may includeinformation that uniquely identifies client device 102, such as a mediaaccess control (MAC) address of client device 102 or an InternetProtocol (IP) address assigned to client device 102. Application data114 may include information that facilitates, or supports, an executionof any of the application programs described herein, such as, but notlimited to, supporting information that enables executable chatbotapplication 108 to authenticate an identity of a user operating clientdevice 102, such as user 101. Examples of this supporting informationinclude, but are not limited to, one or more alphanumeric login orauthentication credentials assigned to user 101, for example, bycomputing system 130, or one or more biometric credentials of user 101,such as fingerprint data or a digital image of a portion of user 101′sface, or other information facilitating a biometric or multi-factorauthentication of user 101. Further, in some instances, application data114 may include additional information that uniquely identifies one ormore of the exemplary application programs described herein, such as acryptogram associated with chatbot application 108.

Additionally, in some examples, client device 102 may include a displayunit 116A configured to present elements to user 101, and an input unit116B configured to receive input from a user of client device 102, suchas user 101. By way of example, display unit 116A may include, but isnot limited to, an LCD display unit, and LED display unit, or otherappropriate type of display unit, and input unit 116B may include, butis not limited to, a keypad, keyboard, touchscreen, fingerprint scanner,voice activated control technologies, stylus, or any other appropriatetype of input unit. Further, in some examples, the functionalities ofdisplay unit 116A and input unit 116B may be combined into a singledevice, such as a pressure-sensitive touchscreen display unit that canpresent elements (e.g., graphical user interface) and can detect aninput from user 101 via a physical touch.

Client device 102 may also include a communications interface 118, suchas a wireless transceiver device, coupled to processor 104.Communications interface 118 may be configured by processor 104, and canestablish and maintain communications with communications network 120via a communications protocol, such as WiFi®, Bluetooth®, NFC, acellular communications protocol (e.g., LTE®, CDMA®, GSM®, etc.), or anyother suitable communications protocol.

Examples of client device 102 may include, but are not limited to, apersonal computer, a laptop computer, a tablet computer, a notebookcomputer, a hand-held computer, a personal digital assistant, a portablenavigation device, a mobile phone, a smartphone, a wearable computingdevice (e.g., a smart watch, a wearable activity monitor, wearable smartjewelry, and glasses and other optical devices that include opticalhead-mounted displays (OHMDs)), an embedded computing device (e.g., incommunication with a smart textile or electronic fabric), and any othertype of computing device that may be configured to store data andsoftware instructions, execute software instructions to performoperations, and/or display information on an interface module,consistent with disclosed embodiments. In some instances, user 101 mayoperate client device 102 and may do so to cause client device 102 toperform one or more operations consistent with the disclosedembodiments.

Referring back to FIG. 1 , computing system 130 may represent acomputing system that includes one or more servers 160 and tangible,non-transitory memory devices storing executable code and applicationmodules. Further, the one or more servers 160 may each include one ormore processor-based computing devices, which may be configured toexecute portions of the stored code or application modules to performoperations consistent with the disclosed embodiments. Additionally, insome instances, computing system 130 can be incorporated into a singlecomputing system. In other instances, computing system 130 can beincorporated into multiple computing systems.

For example, computing system 130 may correspond to a distributed systemthat includes computing components distributed across one or morenetworks, such as communications network 120, or other networks, such asthose provided or maintained by cloud-service providers (e.g., GoogleCloud™, Microsoft Azure™, etc.). In other examples, also describedherein, the distributed computing components of computing system 130 maycollectively perform additional, or alternate, operations that establishan artificial neural network capable of, among other things, adaptivelyand dynamically processing portions of model input to predict candidateinput values associated with corresponding interface elements, orcorresponding combinations of interface elements, within a digitalinterface. The disclosed embodiments are, however, not limited to theseexemplary distributed systems, and in other instances, computing system130 may include computing components disposed within any additional oralternate number or type of computing systems or across any appropriatenetwork.

In some instances, computing system 130 may be associated with, or maybe operated by, a financial institution that provides financial servicesto customers, such as, but not limited to user 101. Further, and asdescribed herein, computing system 130 may also be configured toprovision one or more executable application programs tonetwork-connected devices operable by these customers, such as, but notlimited to, executable chatbot application 108 provisioned to clientdevice 102.

To facilitate a performance of these and other exemplary processes, suchas those described herein, computing system 130 may maintain, within oneor more tangible, non-transitory memories, a data repository 150 thatincludes, but is not limited to, a user database 132, a confidentialdata store 134, chatbot session data store 136, and an interface datastore 138. For example, user database 132 may include structured orunstructured data records that identify and characterize one or moreusers of computing system 130, e.g., user 101. For example, and for eachof the users, the data records of user database 132 may include acorresponding user identifier (e.g., an alphanumeric login credentialassigned to user 101 by computing system 130), and data that uniquelyidentifies one or more devices (such as client device 102) associatedwith or operable by user 101 (e.g., a unique device identifier, such asan IP address, a MAC address, a mobile telephone number, etc., thatidentifies client device 102).

Further, the data records of user database 132 may also link each useridentifier (and in some instances, the corresponding unique deviceidentifier) to one or more elements of profile information correspondingto user 101 and others users of computing system 130, e.g., user 101. Byway of example, the elements of profile information that identify andcharacterize each of the users of computing system 130 may include, butare not limited to, a full name of each of the users and contactinformation associated with each user, such as, but not limited to, amailing address, a mobile number, or an email address. In otherexamples, the elements of profile data may also include values of one ormore demographic characteristics exhibited by or associated withcorresponding ones of the users, such as, but not limited to, an age, agender, a profession, or a level of education characterizing each of theusers.

Confidential data store 134 may include structured or unstructured datathat characterizes an interaction between one or more of the users ofcomputing system 130, such as user 101, and the financial institutionassociated with computing system 130. For example, confidential datastore 134 may include confidential account data and confidentialtransaction data that identify and characterize a balance or transactionhistory of one or more payment instruments, deposit accounts, brokerageaccounts, or other financial services accounts issued to user 101 by thefinancial institution associated with computing system 130. In someinstances, each of the elements of confidential account and transactiondata may be associated with a unique identifier of a corresponding user(e.g., an alphanumeric login credential assigned to user 101) or aunique identifier of a device associated with that corresponding user(e.g., an IP address, MAC address, or mobile telephone number of clientdevice 102). As such, each of the elements of confidential account andtransaction data may be associated with, or linked to, a correspondingdata record within user database 132.

Chatbot session data store 136 may include structured or unstructureddata records that identify and characterize one or more programmaticexchanges of data during chatbot sessions initiated by, or on behalf of,one or more users of computing system 130, such as user 101. Forinstance, the data records of chatbot session data store 136 may includesession data related to one or more previous chatbot sessionsestablished programmatically between an application program executed byclient device 102 (e.g., chatbot application 108, as described herein)and computing system 130. By way of example, and for a particular one ofthese previously established chatbot sessions, the data records ofchatbot session data store 136 may include, but are not limited to,information that identifies a party that initiated or participates inthat previously established chatbot session (e.g., a login credentialassociated with user 101, a device identifier of client device 102, aunique identifier of an executed application program, such as anapplication cryptogram, etc.), a time or date associated with thepreviously established chatbot session, or a duration of thatestablished chatbot session. In other instances, and for the particularone of these previously established chatbot sessions, the data recordsof chatbot session databases 136 may also include raw or processedinformation that identifies and characterizes the data exchangedprogrammatically between client device 102 (e.g., by executed chatbotapplication 108) and computing system 130.

Interface data store 138 may include data records that identify andcharacterize one or more digital interfaces that, when populated andprovisioned to application programs executed by network-connecteddevices and systems within environment 100, facilitate an initiation orexecution of one or more exchanges of data by computing system 130. Insome instances, the data records of interface data store 138 may, foreach of the one or more digital interfaces, include: (i) an interfaceidentifier (e.g., an interface name, an interface type, an alphanumericidentifier, etc.); (ii) layout data that identifies one or more discreteinterface elements (e.g., fillable text boxes, sliding interfaceelements, etc.) and that specifies a sequential position of the discreteinterface elements within corresponding ones of the digital interfaces;and (ii) corresponding elements of information, e.g., metadata, thatcharacterize a type or range of input data associated with each of thediscrete interface elements.

For instance, at least a subset of the digital interfaces may beassociated with an application for one or more financial products orservices capable of provisioning to user 101 by the financial systemassociated with computing system 130. Examples of these digitalinterface include, but are not limited to, digital interfaces thatsupport an application by user 101 for a mortgage product offered by thefinancial institution, an application by user 101 for a line of creditor a credit card offered by the financial institution, or an applicationby user 101 to establish a personal or business banking relationshipwith the financial institution. Additional examples of these digitalinterface may include, but are not limited to, an additional digitalinterface enable user 101 to complete one or more tax forms (e.g., a taxreturn and associated schedules), or an additional digital interfacethat enables user 101 to request or qualify for one or more governmentalor legal services (e.g., a juror qualification form, etc.).

Furthermore, computing system 130 may perform operations that store,within interface data store 138, elements of populated interface dataprovisioned to or obtained from client device 102, e.g., as obtainedthrough data programmatically exchanged with executed chatbotapplication 108 using any of the exemplary processes described herein.Further, computing system 130 may associate each of the stored elementsof populated interface data with the corresponding interface identifier(or identifiers) and with corresponding elements of the layout data andthe metadata, which may facilitate a generation of one or more populateddigital interfaces data programmatically exchanged with executed chatbotapplication 108 during the chatbot sessions described herein, e.g.,without requiring any rendering of the interface elements acrossmultiple display screens.

Referring back to FIG. 1 , computing system 130 may also maintain,within the one or more tangible, non-transitory memories, one or moreexecutable application programs 140, such as, but not limited to, achatbot engine 142, a natural language processing (NLP) engine 144, anda predictive engine 146. When executed by computing system 130 (e.g., bythe one or more processors of computing system 130), chatbot engine 142can perform operations that establish an interactive chatbot sessionwith an application program executed by a network-connected device, suchas chatbot application 108 executed by client device 102. For example,chatbot engine 142 may perform, either alone or in combination with NLPengine 144, any of the exemplary processes described herein to processmessage data received from client device 102 (e.g., based on inputprovided to a digital interface generated and presented by client device102), to adaptively and dynamically parse the message data to establisha meaning and/or a context of the message data and further, to generateand provision, to the chatbot interface generated by chatbot application108 executed by client device 102, a response to the message data via asecure, programmatic interface. In some instances, when presented touser 101 on the chatbot interface (e.g., via display unit 116A of clientdevice 102), the presented response may simulate an ongoing andcontextually relevant dialog between user 101 and an artificially andprogrammatically generated chatbot.

When executed by computing system 130, NLP engine 144 may apply one ormore natural language processing (NLP) algorithms to portions ofreceived message data. Based on the application of these adaptive,statistical, or dynamic natural language processing algorithms, NLPengine 144 may parse the received message data to identify one or morediscrete linguistic elements (e.g., a word, a combination of morphemes,a single morpheme, etc.), and to generate contextual information thatestablishes the meaning or a context of one or more discrete linguisticelements.

Examples of these NLP algorithms may include one or more machinelearning processes, such as, but not limited to, a clustering algorithmor unsupervised learning algorithm (e.g., a k-means algorithm, a mixturemodel, a hierarchical clustering algorithm, etc.), a semi-supervisedlearning algorithm, or a decision-tree algorithm. In other examples, theone or more NLP algorithms may also include one or more artificialintelligence models, such as, but not limited to, an artificial neuralnetwork model, a recurrent neural network model, a Bayesian networkmodel, or a Markov model. Further, the one or more NLP algorithms mayalso include one or more statistical processes, such as those that makeprobabilistic decisions based on attaching real-valued weights toelements of certain input data.

Certain of these exemplary statistical processes, machine learningprocesses, or artificial intelligence models can be trained against, andadaptively improved using, training data having a specified composition,which may be extracted from portions of user database 132, confidentialdata store 134, and/or a chatbot session data store 136, and can bedeemed successfully trained and ready for deployment when a modelaccuracy (e.g., as established based on a comparison with the outcomedata), exceeds a threshold value. Further, although chatbot engine 142and NLP engine 144 are distinctly shown in FIG. 1 , in some examples,the functions of NLP engine 144 may be performed by chatbot engine 142(e.g., NLP engine 144 is part or component of chatbot engine 142).

In some instances, executed predictive engine 146 may perform operationsthat dynamically and adaptively determine candidate values appropriatefor corresponding interface elements of a digital interface, e.g., asrequested by user 101 based on data exchanged programmatically betweenexecuted chatbot engine 142 and executed chatbot application 108 duringany of the exemplary chatbot sessions described herein. For example, thecandidate input value associated with a particular one of the interfaceelements may be consistent with the input data type or range of inputvalues associated the particular interface element. Further, and in someexamples, predictive engine 146 may compute the candidate value for thatparticular interface elements based on an application of one or moredeterministic or stochastic statistical processes, one or more machinelearning processes, or one or more artificial intelligence models tostructured model input that includes, but is not limited to, all orselected portion of the metadata associated with the particularinterface element, selected elements of confidential data maintained onbehalf of user 101 within confidential data store 134, or selectedelements of chatbot session data involving user 101 and maintainedwithin chatbot session data store 136.

For example, the deterministic statistical processes can include, butare not limited to, a linear regression model, a nonlinear regressionmodel, a multivariable regression model, and additionally, oralternatively, a linear or nonlinear least-squares approximation.Examples of the stochastic statistical processes can include, amongother things, a support vector machine (SVM) model, a multipleregression algorithm, a least absolute selection shrinkage operator(LASSO) regression algorithm, or a multinomial logistic regressionalgorithm, and examples of the machine learning processes can include,but are not limited to, an association-rule algorithm (such as anApriori algorithm, an Eclat algorithm, or an FP-growth algorithm) or aclustering algorithm (such as a hierarchical clustering process, ak-means algorithm, or other statistical clustering algorithms). Further,examples of the artificial intelligence models include, but are notlimited to, an artificial neural network model, a recurrent neuralnetwork model, a Bayesian network model, or a Markov model. In someinstances, these stochastic statistical processes, machine learningalgorithms, or artificial intelligence models can be trained against,and adaptively improved using, training data having a specifiedcomposition, which may be extracted from portions of user database 132,confidential data store 134, and/or chatbot session data store 136,along with corresponding outcome data, and can be deemed successfullytrained and ready for deployment when a model accuracy (e.g., asestablished based on a comparison with the outcome data), exceeds athreshold value.

II. Exemplary Computer-Implemented Processes That Dynamically Configure,Populate, and Provision Digital Interfaces Using ProgrammaticallyEstablished Chatbot Sessions

In some examples, to initiate a chatbot session with computing system130, user 101 may provide input to client device 102 (e.g., via inputunit 116B) that requests an execution of a corresponding applicationprogram, such as chatbot application 108 of FIG. 1 . For example, uponexecution by client device 102, chatbot application 108 may generate andrender one or more interface elements for presentation within acorresponding digital interface, such as through display unit 116A. Insome examples, the digital interface may include interface elements thatprompt user 101 to provide, via input unit 116B, input that specifies acorresponding login credential (e.g., an alphanumeric login credentialof user 101, etc.) and one or more corresponding authenticationcredentials (e.g., an alphanumeric password of user 101, a biometriccredential of user 101, etc.).

Based on the provided login and authentication credentials, executedchatbot application 108 may perform operations that authenticate anidentity of user 101 based on copies of locally stored login andauthentication credentials (e.g., as maintained within correspondingportions of device data 112 and application data 114) or based on dataexchanged with one or more network-connected computing systems, such ascomputing system 130. Further, and in response to a successfulauthentication of the identity of user 101, executed chatbot application108 may perform operations that package a unique identifier of user 101(e.g., the login credential), a unique identifier of client device 102(e.g., an IP or MAC address extracted from device data 112) intocorresponding portions of a request to initiate a chatbot session withcomputing system 130. In some instances, executed chatbot application108 may also package data confirming a successful authentication of theidentity of user 101, such as an application cryptogram (e.g., extractedfrom, or generated in accordance with data maintained in, applicationdata 114) into an additional portion of the request.

Client device 102 may transmit the generated request across network 120to computing system 130, e.g., via a secure programmatic interface. Thesecure programmatic interface may receive the generated request, and mayrelay the generated request to chatbot engine 142 of computing system130, which may perform operations that parse the request and extract theuser identifier and the device identifier (and in some instances, thedata confirming the successful authentication of the identity of user101). In some instances, chatbot engine 142 may process the extracteddata (e.g., the user identifier, the device identifier, and/or theconfirmation data), and verify an authenticity or an integrity of thereceived request based on the device identifier or the confirmationdata. Based on the verified authenticity or integrity, chatbot engine142 may perform operations that initiate a chatbot session with executedchatbot application 108, and that generate an additional data recordwithin chatbot session data store 136 that identifies and characterizesthe initiated chatbot session.

By way of example, the newly generated data record may include the useridentifier and the device identifier (and in some instances, theconfirmation data), and may further include a time or date at whichchatbot engine 142 initiated the chatbot session. Further, in someinstances, chatbot engine 142 may perform operations that generate aninitial, introductory message for the chatbot session based on, amongother things, one or more predetermined rules that specify appropriateintroductory messages, the time or date of initiation, and additionally,or alternatively, the user or device identifiers. For example, theintroductory message may include textual content that includes agreeting and that prompts user 101 to further interact with theestablished chatbot session (e.g., “Good morning! How can we help you?),and chatbot engine 142 may perform operations that generate introductorymessage data specifying the introductory message, and that transmit theintroductory message data across network 120 to client device 102, e.g.,through a secure programmatic interface. Chatbot engine 142 may alsoperform operations that store the introductory message data within thenewly generated data record that identifies the chatbot session withinchatbot session data store 136, and that associate the message data withthe user identifier, the device identifier, and additionally, oralternatively, the confirmation data.

In some instances, client device 102 may receive the message data viathe secure programmatic interface, which may route the introductorymessage data to executed chatbot application 108. In response to thereceipt of the introductory message data, executed chatbot application108 may generate and render for presentation a digital interface, e.g.,a chatbot interface, that includes the introductory message data andfacilitates an ongoing and simulated conversation between user 101 and aprogrammatically generated chatbot maintained by computing system 130,as described below in FIG. 2A.

Referring to FIG. 2A, client device 102 may present chatbot interface200 on a corresponding portion of display unit 116A. In some instances,chatbot interface 200 may include a chatbot session area 202, whichdisplays a summary of a current chatbot session, and fillable text box204 allows user 101 to provide input that, after selection of icon 206(e.g., via input unit 116B), will be shown in chatbot session area 202.In some instances, executed chatbot application 108 may performoperations that present all or a portion of the introductory messagedata for presentation within chatbot interface 200, and as illustratedin FIG. 2A, chatbot session area 202 may include introductory message203 (e.g., “Good Morning! How can we help?”). The automatic presentationof introductory message 203 may simulate a conversation between user 101and the programmatic chatbot maintained by computing system 130, and asillustrated in FIG. 2A, introductory message greets user 101 and promptsuser 101 to further interact with the established chatbot session.

In some examples, user 101 may elect to apply for a credit card offeredby the financial institution, and using any of the exemplary processesdescribed herein, user 101 may provide input to a fillable text box ofchatbot interface 200 (e.g., via client device 102) that requests accessto a digital interface associated with the application for the creditcard. For instance, display unit 116A may correspond to apressure-sensitive, touchscreen display unit, and user 101 may provideinput to fillable text box 204, e.g., via a miniaturized “virtual”keyboard presented within digital chatbot interface 200, that specifiesmessage 208, e.g., “I want to apply for a new credit card.”

In other instances, the input to fillable text box 204 may include audiocontent representative of a spoken utterance of message 208, which maybe captured by a corresponding microphone embedded into client device102 (e.g., as a portion of input unit 116B) or in communication withclient device 102 (e.g., across a short-range communications channel,such as Bluetooth™, etc.). Executed chatbot application 108 may receivethe audio content and, based on an application of one or more speechrecognition algorithms or natural language processing (NLP) algorithmsto the audio content, convert the audio content into text correspondingto message 208.

Referring to FIG. 2B, executed chatbot application 108 may process thereceived input, and may present message 208 within a correspondingportion of fillable text box 204. Further, user 101 may provideadditional input to client device 102 that requests a submission ofmessage 208 to the established chatbot session by selecting “Submit”icon 206 (e.g., by establishing contact between a portion of a finger ora stylus and a corresponding portion of a surface of display unit 116Athat corresponds to icon 206, or by uttering one or more predeterminedphrases associated with icon 206, which may be captured by any of theexemplary microphones described herein). Executed chatbot application108 may detect the provided additional input, which requests thesubmission of message 208 to the established chatbot session, and mayperform operations that present all or a portion of message 208 withinchatbot session area 202. In other instances, user 101 may provide inputto input unit 116B that selects “Cancel” icon 207, the detection ofwhich causes executed chatbot application 108 to clear any textcurrently in fillable text box 204 and prevent a submission of message208.

In response to the additional user input that selects “Submit” icon 206,executed chatbot application 108 may perform operations that package allor a portion of message 208 into corresponding portions of session data,along with the unique identifier of user 101 (e.g., the alphanumericlogin credential) and additionally, or alternatively, the unique deviceidentifier (e.g., the IP or MAC address of client device 102 maintainedwithin device data 112). Further, and as described herein, executedchatbot application 108 may also include, within a portion of thereceived message data, an application cryptogram that identifiesexecuted chatbot application 108, e.g., as extracted from applicationdata 114. Executed chatbot application 108 may perform operations thatcause client device 102 to transmit all or a portion of the generatedsession data across network 120 to computing system 130, e.g., using anyappropriate communications protocol.

Referring to FIG. 3 , a secure programmatic interface of computingsystem 130, e.g., application programming interface (API) 301 associatedwith executed chatbot engine 142, may receive session data 302 fromclient device 102. In some instances, and as described herein, sessiondata 302 may include message data 304, which includes textual contentrepresentative of message 208 provided by user 101 as an input tochatbot interface 200 of FIG. 2B (e.g., “I want to apply for a newcredit card”). Session data 302 may also include an identifier 306 ofuser 101 (e.g., an alphanumeric login credential, etc.) and anidentifier 308 of client device 102 (e.g., an IP or MAC address, etc.).Further, session data 302 may include a unique identifier of executedchatbot application 108 (e.g., an application cryptogram) that, in someinstances, may enable computing system 130 to verify an authenticity ofsession data 302.

In some instances, API 301 may be associated with or established byexecuted chatbot engine 142, and may facilitate secure, programmaticcommunications across network 120 between chatbot engine 142 (e.g., asexecuted by computing system 130) and chatbot application 108 (e.g., asexecuted by client device 102). As illustrated in FIG. 3 , API 301 mayreceive and route session data 302 to a session management module 320 ofexecuted chatbot engine 142, which may parse session data 302 to extractone or more of user identifier 306, device identifier 308, orapplication identifier 310. Session management module 320 may alsoperform operations (not illustrated in FIG. 3 ) that verify anauthenticity of session data 302 based on user identifier 306 (e.g.,that user identifier 306 matches a corresponding identifier within userdatabase 132, etc.), device identifier 308 (e.g., based on adetermination that the device identifier is associated with useridentifier 306 within user database 132, etc.), application identifier310 (e.g., that the application-specific cryptogram is associated withan expected structure or format, etc.).

In response to successful verification, session management module 320may perform operations that store session data 302 within one or moretangible, non-transitory memories, e.g., within a portion of chatbotsession data store 136 associated with the established chatbot sessionbetween executed chatbot engine 142 and executed chatbot application108. Session management module 320 may perform operations that generatea programmatic command that executes NLP engine 144, e.g., as providedthrough a corresponding programmatic interface, and that provides all ora portion of message data 304 as an input to executed NLP engine 144. Inother instances (not illustrated in FIG. 3 ), and in response to anunsuccessful verification, session management module 320 may performoperations that generate and transmit, across network 120 to clientdevice 102, an error message indicative of the failed verification, andthat discard session data 302.

Referring back to FIG. 3 , NLP engine 144 may receive message data 304,and may apply any of the exemplary NLP algorithms described herein toall or a portion of message data 304. Based on the application of thesenatural language processing algorithms, NLP engine 144 may identify oneor more discrete linguistic elements (e.g., a word, a combination ofmorphemes, a single morpheme, etc.) within message data 304, and mayestablish a context and a meaning of combinations of the discretelinguistic elements, e.g., based on the identified discrete linguisticelements, relationships between these discrete linguistic elements, andrelative positions of these discrete linguistic elements within messagedata 304. In some instances, NLP engine 144 may generate linguisticelement data 322, which includes each discrete linguistic element, andcontextual information 324 that specifies the established context ormeaning of the combination of the discrete linguistic elements.

As described herein, message data 304 may be representative of message208 provided by user 101 as an input to chatbot interface 200 of FIG. 2, e.g., “I want to apply for a new credit card.” Based on theapplication of the exemplary NLP algorithms described herein to messagedata 304, NLP engine 144 may parse message data 304 and extract discretelinguistic elements (e.g., discrete words) that include, but are notlimited to, “I,” “want,” “to,” “apply,” “for,” “a,” “new,” “credit,” and“card,” each of which may be packaged into a corresponding portion oflinguistic element data 322. Further, and based on any of theseexemplary natural language processing algorithms described herein to thediscrete linguistic elements, e.g., alone or in combination, NLP engine144 may determine that message 208 corresponds to a request to access adigital interface associated with, and facilitating, the application forthat new credit card, and may package contextual data indicative of thedetermination into a corresponding portion of contextual information324. In some instances, the contextual data may characterize a nature orpurpose of message 208 (e.g., the request for the digital interface) andmay include one or more identifiers associated with the requesteddigital interface, e.g., that enable computing system 130 to accesselements of locally maintained interface data associated with therequested digital interface.

Executed NLP engine 144 may provide linguistic element data 322 andcontextual information 324 as inputs to an interface selection module326 that, when executed by computing system 130, performs any of theexemplary processes described herein to identify the digital interfacerequested by message, e.g., based on portions of linguistic element data322 or contextual information 324, and to extract one or more locallymaintained elements of interface data associated with the identifieddigital interface, e.g., as maintained within interface data store 138of data repository 150. By way of example, and as described herein, theextracted elements of interface data main include, for the identifieddigital interface: (i) layout data that identifies one or more discreteinterface elements (e.g., fillable text boxes, sliding interfaceelements, etc.) and that specifies a sequential position of the discreteinterface elements within the digital interface; and (ii) correspondingelements of information, e.g., metadata, that characterize a type orrange of input data associated with each of the discrete interfaceelements.

As illustrated in FIG. 3 , interface selection module 326 may receivelinguistic element data 322 or contextual information 324, e.g., asoutputs from NLP engine 144, and may perform operations that storelinguistic element data 322 and contextual information 324 within one ormore tangible, non-transitory memories, e.g., within a portion ofchatbot session data store 136 that includes session data 302. Further,interface selection module 326 may access interface data store 138, andmay perform operations that, based on portions of linguistic elementdata 322 and/or contextual information 324, identify one or moreelements of the locally maintained interface data that are associatedwith digital interface requested in message 208, e.g., the digitalinterface associated with the application for the new credit card.

By way of example, executed interface selection module 326 may access anelement 328 of digital interface data maintained within interface datastore 138. Interface data element 328 may be associated with aparticular digital interface associated with, available to, orprovisionable to user devices by computing system 130, and interfacedata element 328 may include one or more interface identifiers 330 ofthe particular digital interface (e.g., an interface name or aninterface type, etc.), along with layout data 332 and metadata 334associated with the particular digital interface.

As described herein, layout data 332 may also include discrete dataelements (e.g., layout data elements 332A, 332B, . . ., 332N of FIG. 3), each of which identify and characterize a corresponding one of theinterface elements of the particular digital interface (e.g., fillabletext boxes, sliding interface elements, etc.) and further, specify asequential position of the corresponding interface element within theparticular digital interface. For example, each of the discrete dataelements of layout data 332 may include indexing information (e.g., aflag, etc.) that specifies the sequential position of the correspondinginterface element within the particular digital interface and in someinstances, identifies an dependency or a relationship between an inputvalue of the corresponding interface element and input values of otherinterface elements within the particular digital interface (e.g., avalue of a total income may correspond to a summation of wages andinvestment income, etc.). Further, each of the discrete elements oflayout data 332 (e.g., layout data elements 332A, 332B, . . . 332N), mayalso be associated with a corresponding element of metadata 334 (e.g.,metadata elements 334A, 334B, . . . 334N of FIG. 3 ), whichcharacterizes a type or range of input data associated with thecorresponding interface element.

Based on a comparison between interface identifiers 330 and the portionsof contextual information 324 and/or linguistic element data 322,executed interface selection module 326 may determine that theparticular digital interface associated with interface data element 328represents the digital interface requested by message 208, e.g., thatthe particular digital interface corresponds to the requested digitalinterface for the credit card application. For instance, contextualinformation 324 may include data that identifies an interface typeassociated with the requested digital interface (e.g., the credit cardapplication), and may also identify the particular credit cardreferenced in message 208. In some examples, executed interfaceselection module 326 may parse interface identifiers 330, and based on adetermination that at least one of interface identifiers 330 include orreference the interface type or the particular credit card, establishthat interface data element 328 is associated with the digital interfacerequested by message 208. Executed interface selection module 326 mayperform operations that extract interface data element 328 frominterface data store 138, and provide interface data element 328 as aninput to predictive engine 146 that, when executed by computing system130, performs any of the exemplary processes described to compute acandidate input value for each of the interface elements within therequested digital interface based on, among other things, correspondingelements of layout data 332 and metadata 334.

In other examples, executed interface selection module 326 may determinethat none of the elements of interface data maintained within interfacedata store 138 are associated with, or representative of, the requesteddigital interface, or that multiple elements of interface datamaintained within interface data store 138 are potentially associatedwith, or potentially representative of, the requested digital interface(e.g., based on ambiguities in the potions of contextual information 324and/or linguistic element data 322, etc.). Based on the determined lackof interface data elements associated with the requested digitalinterface, or based on the determined plurality of interface dataelements potentially associated with the requested digital interface,executed interface selection module 326 may generate and transmitprogrammatically an error flag to executed chatbot engine 142 (notillustrated in FIG. 3 ), which may perform additional operations thatclarify user 101′s request based on additional message dataprogrammatically exchanged with executed chatbot application 108 duringthe existing chatbot session (also not illustrated in FIG. 3 ).

Referring back to FIG. 3 , executed predictive engine 146 may receiveinterface data element 328, may perform operations that parse layoutdata 332 to identify, and extract an element of layout data 332, e.g.,layout data element 332A, and a corresponding element of metadata, e.g.,metadata element 334A, associated with a corresponding one of theinterface elements disposed at a first sequential position within therequested digital interface, e.g., a “first” interface element. Forexample, executed predictive engine 146 may perform operations thataccess the indexing information included within each of the discretedata element of layout data 332, and based on the indexing information,establish that layout data element 332A represents, and is associatedwith, the first interface element within the requested digitalinterface.

Executed predictive engine 146 may also parse metadata element 334A toobtain information that characterizing a type, range, or format of inputdata associated with the first interface element. For example, and basedon metadata element 334A, executed predictive engine 146 may establishthat input data appropriate to the first interface elements represents alegal name of user 101 (e.g., as specified within a correspondinggovernment-issued identifier, such as a passport), and that theappropriate input data format includes alphanumeric input having apredetermined minimum length (e.g., two characters) and a predeterminedmaximum length (e.g., sixty-four characters). Executed predictive engine146 may also perform any of the exemplary processes described herein tocompute a candidate input value 336A for the first interface elementbased on the data type or data format specified within metadata element334A.

Executed predictive engine 146 may also perform operations that accesssession data 302 associated with the established chatbot session (e.g.,as maintained within chatbot session data store 136), and extract useridentifier 306, which identifies user 101 (e.g., the alphanumeric logincredential of user 101) and additionally, or alternatively, deviceidentifier 308, which identifies client device 102 (e.g., the IP or MACaddress of client device 102). In some instances, executed predictiveengine 146 may access user database 132 and identify one or more datarecords 338 that include, or reference user identifier 306 (andadditionally, or alternatively, device identifier 308). Executedpredictive engine 146 may perform operations that extract, from datarecords 338, the legal name of user 101 (e.g., “John Q. Stone”) and maypackage the extracted legal name of user 101 into candidate input value336A, along with indexing information characterizing the sequentialposition of the first interface element within the requested digitalinterface.

In other instances, executed predictive engine 146 may performadditional operations to modify the extracted legal name of user 101based on the appropriate input data format (e.g., to truncate theextracted legal name in accordance with the predetermined maximumlength), and to package the modified legal name of user 101 intocandidate input value 336A, along with the indexing information.Further, executed predictive engine 146 may also package candidate inputvalue 336A into a corresponding portion of output data 336 of executedpredictive engine 146.

In some examples, executed predictive module 146 may perform any of theexemplary processes described herein to identify and extract anadditional element of layout data 332, e.g., layout data element 332B,and a corresponding element of metadata, e.g., metadata element 334B,associated with a corresponding one of the interface elements disposedat a second sequential position within the requested digital interface,e.g., a “second” interface element. As described herein, executedpredictive engine 146 may perform operations that access the indexinginformation included within each of the discrete data elements of layoutdata 332, and based on the indexing information, establish that layoutdata element 332B represents, and is associated with, the secondinterface element within the requested digital interface.

Executed predictive engine 146 may also parse metadata element 334B toobtain information that characterizing a type or range of input dataassociated with the second interface element. For example, and based onmetadata element 334B, executed predictive engine 146 may establish thatinput data appropriate to the second interface elements represents acurrent street address of user 101 (e.g., as specified within acorresponding government-issued identifier, such as a passport), andexecuted predictive engine 146 may perform any of the exemplaryprocesses described herein to identify and extract the current streetaddress of user 101 from data records 338 (e.g., associated with useridentifier 306 or device identifier 308 with user database 132), and topackage the extracted street address into candidate input value 336B.

The disclosed embodiments are, however, not limited these examples ofinput data, and in other instances, the additional input dataappropriate to the second interface element (or to other sequentiallydisposed interface elements within the requested digital interface) mayinclude an additional or alternate element of profile data, confidentialdata, or chatbot session data maintained locally by computing system 130that is consistent with the input data type or format specified withinmetadata element 334B. Examples of the additional input data appropriateto the second interface element (or to the other sequentially disposedinterface elements within the requested digital interface) may include,but is not limited to, a current or city of residence of user 101, acurrent zip or postal code of user 101, a current employer of user 101,a birthdate of user 101, or a government-issued identifier held by user101 (e.g., a driver’s license number, a social security number, etc.),and the additional input data may be maintained within data records ofone or more of user database 132, confidential data store 134, orchatbot session data store 136, e.g., in conjunction with useridentifier 306 or device identifier 308.

In other instances, and in addition to the exemplary processes describedherein that extract the appropriate input data from one or more oflocally maintained data repositories, predictive engine 146 may alsoperform operations that dynamically and adaptively predict theadditional input data appropriate to the second interface element (or toother sequentially disposed interface elements within the requesteddigital interface) based on an application of one or predictive modelsto model input associated with the second interface element (or withothers of the sequentially disposed interface elements within therequested digital interface). By way of example, and for the secondinterface element described herein, the model input may include, but isnot limited to, all or a selected portion of metadata element 334B(e.g., that characterizes the type, range, or format of the appropriateinput data associated with the second interface element) and additionalelements of profile data, confidential data, or chatbot session dataassociated with user 101 (e.g., as extracted from, or selectivelyderived from data maintained within, one or more of user database 132,confidential data store 134, or chatbot session data store 136.

The model input may also include elements of profile data, confidentialdata, or chatbot session data associated with additional users ofcomputing system 130 that are demographically similar to user 101 (e.g.,as extracted from, or selectively derived from data maintained within,one or more of user database 132, confidential data store 134, orchatbot session data store 136). Further, in some instances, the modelinput may include data that characterizes an interaction of theseadditional users within the requested data interface, e.g., as extractedfrom, or derived from, corresponding portions of interface data store138. The disclosed embodiments are, however, not limited to theseexamples of structured model input, and in other instances, the modelinput associated with the second interface element (or with any of theother sequentially disposed interface elements within the requesteddigital interface) may include any additional or alternate dataassociated with user 101, the additional users, or the interfaceelements that would be appropriate to the one or more predictive models.

By way of example, and as described herein, the predictive models may bebased on one, or more, of a deterministic or stochastic statisticalprocess, a machine learning processes, or an artificial intelligencemodel. For example, the deterministic statistical process can include,but is not limited to, a linear regression model, a nonlinear regressionmodel, a multivariable regression model, and additionally, oralternatively, a linear or nonlinear least-squares approximation.Examples of the stochastic statistical process can include, among otherthings, a support vector machine (SVM) model, a multiple regressionalgorithm, a least absolute selection shrinkage operator (LASSO)regression algorithm, or a multinomial logistic regression algorithm,and examples of the machine learning process can include, but are notlimited to, an association-rule algorithm (such as an Apriori algorithm,an Eclat algorithm, or an FP-growth algorithm) or a clustering algorithm(such as a hierarchical clustering process, a k-means algorithm, orother statistical clustering algorithms). Further, examples of theartificial intelligence models include, but are not limited to, anartificial neural network model, a recurrent neural network model, aBayesian network model, or a Markov model. As described herein, thesestochastic statistical processes, machine learning processes, orartificial intelligence models can be trained against, and adaptivelyimproved using, training data having a specified composition andcorresponding outcome data, and can be deemed successfully trained andready for deployment when a model accuracy (e.g., as established basedon a comparison with the outcome data), exceeds a threshold value.

As illustrated in FIG. 3 , predictive engine 146 may obtain modellingdata 344 (e.g., from one or more tangible, non-transitory memories) thatspecifies a composition and/or a structure of the model input associatedwith each of the predictive models, as such, corresponding ones of thedeterministic or stochastic statistical processes, machine learningprocesses, or artificial intelligence models. In some examples, asdescribed herein, the structure or composition of model input may bemodel specific (e.g., tailored to a specific compositional requirementof the deterministic or stochastic statistical processes, machinelearning processes, or artificial intelligence models described herein).Additionally, or alternatively, the composition or structure of themodel input may be specific to user 101 or to the requested digitalinterface associated with the credit card application).

In some examples, executed predictive engine 146 may perform operationsthat generate the model input in accordance with the composition orstructure specified by modelling data 344, and may apply the one or morepredictive models (e.g., one of more of the deterministic or stochasticstatistical processes, machine learning processes, or artificialintelligence models) to each of the discrete elements of the generatedmodel input. Based on the application of the one or more predictivemodels to discrete elements of input data, executed predictive engine146 may determine a candidate input value 336B for the second interfaceelement (or for any of the other sequentially disposed interfaceelements within the requested digital interface). In some instances,executed predictive engine 146 may package the candidate input value,e.g., as predicted based on the application of the one or morepredictive models to the generated model input, into correspondingportions of output data 336.

By way of example, the second interface element may be associated with arequested amount of credit associated with the new credit card account,and the one or more predictive models may include an artificial neuralnetwork model implemented by the distributed computing components ofcomputing system 130, e.g., as nodes of the artificial neural network.Further, modelling data 344 may associate the artificial neural networkmodel with corresponding elements of model input that include, but arenot limited to: a portion of metadata element 334B that identified theappropriate input data (e.g., the requested amount of credit); profiledata specifying a current residence of user 101 (e.g., as maintainedwithin data records 338 of user database 132); confidential account dataspecifying a current balance of one or more financial services accountsissued to user 101 by the financial institution (e.g., as maintainedwithin data records 340 of confidential data store 134); and datacharacterizing the amounts of credit requested by additional usersinteracting with the requested digital interface (e.g., as maintainedwithin data records 342 of interface data store 138).

In some instances, executed predictive engine 146 may perform operationsthat, based on modelling data 344, access and extract the elements ofprofile data and confidential account data associated with user 101(e.g., from respective ones of user database 132 and confidential datastore 134), identify one or more additional users that aredemographically similar to user 101 (e.g., based on detectedsimilarities between portions of the profile data maintained within userdatabase 132), and access and extract the data characterizing theamounts of credit requested by the additional users (e.g., as maintainedwithin interface data store 138). Executed predictive engine 146 maypackage the portion of metadata element 334B, the extracted profile dataand confidential account data associated with user 101, and theextracted data characterizing the amounts of credit requested by theadditional users into corresponding portions of the model input, mayprovide each of the elements of the generated model input to acorresponding one of the nodes of the artificial neural network, e.g.,to apply the artificial neural network model to the generated modelinput.

Based on the application of the artificial neural network model to thegenerated model input, executed predictive engine 146 predict acandidate amount of credit of $75,000 for user 101, and may package thecandidate credit amount of $75,000 into candidate input value 336B,along with indexing information that characterizes the sequentialposition of the second interface element within the requested digitalinterface. Further, executed predictive engine 146 may also packagecandidate input value 336B into a corresponding portion of output data336, e.g., at a storage location corresponding to the sequentialposition of the second data element within the requested digitalinterface.

Further, executed predictive engine 146 may perform any of the exemplaryprocesses described herein to compute a candidate input value for eachadditional or alternate interface element disposed a correspondingsequential position within the requested digital interface, and maypackage each of these additional or alternate candidate input values,each which include indexing information indicative of the sequentialposition of the corresponding interface element within the digitalinterface, within a portion of output data 336, e.g., as discretecandidate input values 336A, 336B, . . . 336N. In some instances,executed predictive engine 146 also perform operations that packageinterface identifiers 330 of the requested digital interface into acorresponding portion output data 336, e.g., within a header portion.

As illustrated in FIG. 3 , executed predictive engine 146 may provideoutput data 336, which includes the discrete candidate input valuesassociated with respective ones of the interface elements within therequested digital interface (e.g., candidate input values 336A, 336B, .. . 336N) and interface identifiers 330, as an input to executed chatbotengine 142. In some instances, described below in reference to FIGS.4A-4E, executed chatbot engine 142 may perform operations that verify anaccuracy of each of the candidate input values based on sequential andsuccessive elements of message data programmatically exchanged withexecuted chatbot application 108 during the established chatbot session,and that populate the interface elements of the requested digitalinterface (e.g., the digital interface associated with the credit cardapplication) based on corresponding ones of the verified input values.

Referring to FIG. 4A, a message generation module 402 of executedchatbot engine 142 may receive output data 336 from predictive engine146. In some instances, executed chatbot engine 142 may obtain interfaceidentifiers 330 of the requested digital interface (e.g., the digitalinterface associated with the credit card application) from output data336, and may access interface data store 138, and extract thecorresponding elements of layout data 332 and metadata 334 associatedwith interface identifiers 330. As described herein, layout data 332 mayinclude discrete data elements (e.g., layout data elements 332A, 332B, .. . 332N), each of which identify and characterize a corresponding oneof the interface elements of the requested digital interface andfurther, include indexing information that specifies the sequentialposition of the corresponding interface element within the requesteddigital interface. Further, metadata 334 may include discrete metadataelements (e.g., metadata elements 334A, 334B, ... 334N) thatcharacterize the type or range of input data associated withcorresponding ones of the interface elements within the requesteddigital interface, and as described herein, each of the discretemetadata elements 334A, 334B, . . . 334N may be associated with acorresponding one of the discrete data elements of layout data 332.

In some instances, message generation module 402 may perform operationsthat obtain, from layout data 332, metadata 334, and output data 336,respective ones of the layout data element, the metadata element, andthe candidate input value associated with the corresponding one of theinterface elements disposed at the first sequential position within therequested digital interface, e.g., the first interface element describedherein. For example, and based on the indexing information includedwithin each of layout data elements 332A, 332B, . . . 332N, messagegeneration module 402 may establish an association between layout dataelement 332A and the first interface element of the requested digitalinterface, and may extract layout data element 332A from layout data332. Message generation module 402 may also identify, and extract frommetadata 334, metadata element 334A, which may be associated with layoutdata element 332A and further, with the first interface element.Additionally, and based on the indexing information included within eachof candidate input values 336A, 336B, . . . 336N, message generationmodule 402 may establish an association between candidate input value336A and the first interface element of the requested digital interface,and may extract candidate input value 336A from output data 336.

Based on layout data element 332A, metadata element 334A, and candidateinput value 336A, message generation module 402 may generate one or moreadditional elements of message data that, when exchangedprogrammatically with executed chatbot application 108 during theestablished chatbot session, not only responds to message 208 (e.g., “Iwant to apply for a new credit card”), but also enables user 101 tointeract with the first interface element of the requested digitalinterface by, among other things, confirming an accuracy of candidateinput value 336A associated with the first interface element, e.g.,based on additional input provided to client device 102 during theestablished and ongoing chatbot session. As illustrated in FIG. 4A,message generation module 402 may programmatically generate textual data404 that refers to, and response to, message 208 (e.g., based onportions of message data 304 maintained within chatbot session datastore 136), and that prompts user 101 to confirm the accuracy ofcandidate input value 336A, e.g., the candidate legal name of user 101.

In some instances, textual data 404 may include one or more elements ofpredetermined textual content, which may be maintained locally bycomputing system 130 within data repository 150 (not illustrated in FIG.4A), or may be generated by message generation module 402 based on anapplication of one or more adaptively trained machine learning processesor artificial intelligence models (e.g., the artificial neural networkdescribed herein, etc.) to data that includes, but is not limited to,portions of message data 304 and metadata element 334A. Additionally, insome instances, textual data 404 may also include portions of metadataelement 334A, which identifies and characterizes the first interfaceelement or candidate input value 336A.

Message generation module 402 may package textual data 404 and candidateinput value 336A into corresponding potions of response message data406, may perform operations that cause computing system 130 to transmitresponse message data 406 across network 120 to client device 102, e.g.,via the corresponding communications interface using any appropriatecommunications protocol. In some instances, not illustrated in FIG. 4A,message generation module 402 may also package data associated with, oridentifying, the established and ongoing chatbot session into responsemessage data 406, such as a session identifier or a cryptogramassociated with chatbot engine 142.

A secure programmatic interface of client device 102, e.g., applicationprogramming interface (API) 408, may receive and route response messagedata 406 to a processing module 410 of executed chatbot application 108.API 408 may be associated with or established by executed chatbotapplication 108, and may facilitate secure, programmatic communicationsacross communications network 120 between chatbot application 108 (e.g.,as executed by client device 102) and chatbot engine 142 (e.g., asexecuted by computing system 130).

Processing module 410 may receive response message data 406, and mayperform operations that store response message data 406 within one ormore tangible, non-transitory memories, e.g., within memory 105.Further, and based on portions of response message data 406 (e.g., theinformation identifying the established and ongoing chatbot session,such as the session identifier or cryptogram), processing module 410 maydetermine that response message data 406 represents a new message withinthe ongoing and simulated conversation between user 101 and theprogrammatically generated chatbot maintained by computing system 130(e.g., a new message within the established and ongoing chatbotsession).

In some instances, processing module 410 may parse response message data406 to extract textual data 404 and candidate input value 336A, and mayroute candidate textual data 404 and candidate input value 336A todisplay unit 116A, which may present textual data 404 and candidateinput value 336A within a corresponding portion of chatbot interface200, e.g., as part of the ongoing and simulated conversation. Referringto FIG. 4B, and when presented within chatbot session area 202 ofchatbot interface 200, textual data 404 may establish a new message 412that includes textual content 414A confirming the prior request for thenew credit card by user 101 (e.g., “Great! Let’s get started with yourapplication”). In some instances, new message 412 may also includeadditional textual content 414B that, when presented in conjunction withcandidate input value 336A, prompts user 101 to provide additional inputto client device 102 confirm an accuracy of candidate input value 336Aof the first interface element of the requested digital interface, or tomodify candidate input value 336A to reflect an accurate input to thefirst interface element.

In some examples, described in reference to FIG. 4B, user 101 maydetermine that candidate input value 336A, as presented within chatbotsession area 202, accurately reflects user 101′s full legal name (e.g.,“John Q. Stone”), and user 101 may provide additional input to clientdevice 102 that confirms the determined accuracy of candidate inputvalue 336A, e.g., by establishing contact between a finger or a stylusand a portion of a surface of a pressure-sensitive, touchscreen displayunit that corresponds to a confirmation icon 416 present within chatbotinterface 200. In other examples, user 101 may detect one or more errorsin candidate input value 336A presented within new message 412 inconjunction with additional textual content 414B. Responsive to the oneor more detected errors, user 101 may provide input to fillable text box204 (e.g., via a miniaturized “virtual” keyboard presented withinchatbot interface 200, as described herein) that accurately reflects thefull legal name of user 101, and may provide further input to clientdevice 102 that confirms the modification to candidate interface element336A, e.g., by establishing contact between the finger or the stylus andthe portion of the surface of the pressure-sensitive, touchscreendisplay unit that corresponds to confirmation icon 416.

Referring to FIG. 4C, input unit 116B may receive input 417 from user101, and may route input data 418 that characterizes received input 417to a triggering module 420 of executed chatbot application 108. Forexample, input data 417 may identify one or more spatial positions ofuser 101′s established contact along the surface of thepressure-sensitive, touchscreen display unit, and may also identify aduration of that established content. In some instances, triggeringmodule 420 may perform operations that establish that user 101 selectedconfirmation icon 416 within chatbot interface 200, e.g., based on adetermination that the one or more contact positions correspond to apresented position of confirmation icon 416 within chatbot interface200.

Based on the determination that user 101 selected confirmation icon 416,triggering module 420 may perform further operations that establish,based on input data 418, whether the selection of confirmation icon 416represents a confirmation of the determined accuracy of candidate inputvalue 336A (e.g., the full name of user 101), or alternatively, arequest to modify candidate input value 336A to correct one or moredetected errors or omissions. By way of example, triggering module 420may parse input data 418 to identify a presence, or an absence, ofadditional data modifying candidate input value 336A.

If, for example, triggering module 420 were unable to identify thepresence of the additional data within input data 418, triggering module420 may establish that user 101′s selection of confirmation icon 416represents the confirmation of the determined accuracy of candidateinput value 336A, and triggering module 420 may generate a data flag(e.g., confirmation flag 422) indicative of the confirmation of thedetermined accuracy, and may provide confirmation flag 422 as an inputto a messaging module 424 of executed chatbot engine 142. As illustratedin FIG. 4C, messaging module 424 may receive confirmation flag 422,which confirms the determined accuracy of candidate input value 336A,and may package confirmation flag 422 and candidate input value 336A(e.g., as maintained within and extracted from memory 105) intocorresponding portions of a confirmation message 426.

In some instances, confirmation message 426 may also include the uniqueidentifier of user 101 (e.g., the alphanumeric login credential of user101), the unique device identifier of client device 102 (e.g., the IP orMAC address of client device 102) and additionally, or alternatively,the unique identifier of chatbot application 108 (e.g., theapplication-specific cryptogram described herein). Messaging module 424may perform additional operations that cause client device 102 totransmit confirmation message 426 across network 120 to computing system130, e.g., via communications interface 118 using any appropriatecommunications protocol.

In other examples, not illustrated in FIG. 4C, triggering module 420 maydetect the presence one or more elements of the additional data withininput data 418, which reflect a requested modification to candidateinput value 336A. Triggering module 420 may perform further operationsthat generate an additional data flag, e.g., a modification flag,indicative of the requested modification, and provide the modificationflag and the one or more elements of additional data as inputs tomessaging module 424, which may perform any of the exemplary processesdescribed herein to package the modification flag and the one or moreelements of additional data into corresponding portions of amodification message. As described herein, messaging module 424 mayperform additional operations that cause client device 102 to transmitthe modification message across network 120 to computing system 130,e.g., via communications interface 118 using any appropriatecommunications protocol.

Referring back to FIG. 4C, a secure programmatic interface of computingsystem 130, such as an application programming interface (API) 428, mayreceive and route confirmation message 426 to an interface provisioningmodule 430 executed by computing system 130. API 428 may be associatedwith or established by executed interface provisioning module 430, andmay facilitate secure, programmatic communications across communicationsnetwork 120 between interface provisioning module 430 (e.g., as executedby computing system 130) and chatbot application 108 (e.g., as executedby client device 102).

As described herein, confirmation message 426 may include confirmationflag 422 and candidate input value 336A. In some instances, confirmationmessage 426 may also include the unique identifier of user 101 (e.g.,the alphanumeric login credential of user 101), the unique deviceidentifier of client device 102 (e.g., the IP or MAC address of clientdevice 102) and additionally, or alternatively, the unique identifier ofchatbot application 108 (e.g., the application-specific cryptogramdescribed herein). By way of example, executed interface provisioningmodule 430 may perform operations (not illustrated in FIG. 4C) thatparse confirmation message 426 and extract the unique identifiers ofuser 101, client device 102, or executed chatbot application 108, andperform operations that authenticate an identity of user 101 or clientdevice 102 (e.g., based on portions of the unique identifiers of user101 or client device 102) or verify an authenticity of confirmationmessage 426 (e.g., based on the unique identifier of executed chatbotapplication 108, such as an application cryptogram).

If executed interface provisioning module 430 were unable toauthenticate the identity of user 101 or client device 102, or to verifythe authenticity of confirmation message 426, executed interfaceprovisioning module 430 may generate an error message indicative of thefailed authentication or verification, which computing system 130 maytransmit back across network 120 to client device 102. Further, executedinterface provisioning module 430 may perform operations that discardreceived confirmation message 426, and await additional provisioningrequests generated by client device 102.

In other instances, and in response to a successful authentication ofthe identity of user 101 or client device 102, and/or a successfulverification of the authenticity of confirmation message 426, executedinterface provisioning module 430 may parse confirmation message 426 toextract confirmation flag 422 and candidate input value 336A. Executedinterface provisioning module 430 may process confirmation flag 422,which establishes the confirmation of the accuracy of candidate inputvalue 336A by user 101, and perform operations that generate, for thefirst interface element of the requested digital interface, an element432A of populated interface data 432 that includes the now-confirmedcandidate input value 336A. As illustrated in FIG. 4C, executedinterface provisioning module 430 may store element 432A of populatedinterface data 432 within a portion of interface data store 138, andassociate element 432A with interface identifiers 330 of the requesteddigital interface and with corresponding elements of layout data 332 andmetadata 334 associated with the requested digital interface (e.g.,layout data element 332A and metadata element 334A).

Further, in some examples, executed interface provisioning module 430may provide confirmation flag 422 as an input to message generationmodule 402 of executed chatbot engine 142, which may perform any of theexemplary processes described herein to obtain, from layout data 332,metadata 334, and output data 336, respective ones of the layout dataelement, the metadata element, and the candidate input value associatedwith the corresponding one of the interface elements disposed at thesecond sequential position within the requested digital interface, e.g.,the second interface element described herein. For example, and based onthe indexing information included within each of layout data elements332A, 332B, . . . 332N, message generation module 402 may establish anassociation between layout data element 332B and the second interfaceelement of the requested digital interface, and may extract layout dataelement 332B from layout data 332. Message generation module 402 mayalso identify, and extract from metadata 334, metadata element 334B,which may be associated with layout data element 332B and further, withthe second interface element. Additionally, and based on the indexinginformation included within each of candidate input values 336A, 336B, .. . 336N, message generation module 402 may establish an associationbetween candidate input value 336B and the second interface element ofthe requested digital interface, and may extract candidate input value336B from output data 336.

Based on layout data element 332B, metadata element 334B, and candidateinput value 336B, message generation module 402 may perform operationsthat generate one or more additional elements of message data that, whenexchanged programmatically with executed chatbot application 108 duringthe established chatbot session, enables user 101 to interact with thesecond interface element of the requested digital interface by, amongother things, confirming an accuracy of candidate input value 336Bassociated with the second interface element, e.g., through onadditional input provided to client device 102 during the establishedand ongoing chatbot session. As illustrated in FIG. 4C, messagegeneration module 402 may perform any of the exemplary processesdescribed herein to programmatically generate textual data 434 thatprompts user 101 to confirm the accuracy of candidate input value 336B,e.g., the candidate legal name of user 101.

Message generation module 402 may package textual data 434 and candidateinput value 336B into corresponding potions of response message data436, may perform operations that cause computing system 130 to transmitresponse message data 436 across network 120 to client device 102, e.g.,via the corresponding communications interface using any appropriatecommunications protocol. In some instances, not illustrated in FIG. 4C,message generation module 402 may also package data associated with, oridentifying, the established and ongoing chatbot session into responsemessage data 436, such as a session identifier or a cryptogramassociated with chatbot engine 142.

In some instances, API 408 of client device 102 may receive and routeresponse message data 436 to processing module 410 of executed chatbotapplication 108, which may store response message data 436 within one ormore tangible, non-transitory memories, e.g., within memory 105.Further, and based on portions of response message data 436 (e.g., theinformation identifying the established and ongoing chatbot session,such as the session identifier or cryptogram), processing module 410 maydetermine that response message data 436 represents a new message withinthe ongoing and simulated conversation between user 101 and theprogrammatically generated chatbot maintained by computing system 130(e.g., a new message within the established and ongoing chatbotsession).

Processing module 410 may parse response message data 436 to extracttextual data 434 and candidate input value 336B, and may route textualdata 434 and candidate input value 336B to display unit 116A forpresentation within a corresponding portion of chatbot interface 200.Referring to FIG. 4D, and when presented within chatbot session area 202of chatbot interface 200, textual data 434 may establish a new message438 including textual content 440 that, when presented in conjunctionwith candidate input value 336B, prompts user 101 to provide additionalinput to client device 102 that confirms an accuracy of candidate inputvalue 336B, or that modify candidate input value 336B to reflect anaccurate input to the second interface element.

In some examples, described in reference to FIG. 4D, user 101 maydetermine that candidate input value 336B (e.g., the predicted streetaddress of “226 Park Street”) includes one or more errors or omissions.Responsive to the one or more detected errors or omissions, user 101 mayprovide input to fillable text box 204 (e.g., via a miniaturized“virtual” keyboard presented within chatbot interface 200, as describedherein) that accurately reflects the correct street address 442 of user101, e.g., “224 Park Street.” As described herein, user 101 may alsoprovide input to client device 102 that confirms the modification tocandidate input value 336B, e.g., by establishing contact between thefinger or the stylus and the portion of the surface of thepressure-sensitive, touchscreen display unit that corresponds toconfirmation icon 416.

Referring to FIG. 4E, input unit 116B may receive input 443 from user101, and may route input data 444 that characterizes received input 443to triggering module 420 of executed chatbot application 108, which mayperform any of the exemplary processes described herein to establishthat user 101 selected confirmation icon 416 within chatbot interface200, e.g., based on a determination that the one or more contactpositions correspond to a presented position of confirmation icon 416within chatbot interface 200. Based on the determination that user 101selected confirmation icon 416, triggering module 420 may perform any ofthe exemplary processes described herein to establish, based on inputdata 444, whether the selection of confirmation icon 416 represents aconfirmation of the determined accuracy of candidate input value 336B(e.g., the street address of user 101), or alternatively, a request tomodify candidate input value 336B to correct one or more detected errorsor omissions.

By way of example, triggering module 420 may parse input data 418 anddetect a presence of additional data 446 that modifies candidate inputvalue 336B and specifies the correct street address of user 101 (e.g.,224 Park Street). Based on the detection of additional data 446,triggering module 420 may perform operations that generate a data flag,e.g., a modification flag 448, indicative of the requested modification,and provide modification flag 448 and additional data 446 as inputs tomessaging module 424 of executed chatbot engine 142. As illustrated inFIG. 4D, messaging module 424 may receive modification flag 448, whichconfirms the modification to candidate input value 336B, and may packagemodification flag 448 and additional data 446 into correspondingportions of a modification message 450.

In some instances, modification message 450 may also include the uniqueidentifier of user 101 (e.g., the alphanumeric login credential of user101), the unique device identifier of client device 102 (e.g., the IP orMAC address of client device 102) and additionally, or alternatively,the unique identifier of chatbot application 108 (e.g., theapplication-specific cryptogram described herein). Messaging module 424may perform additional operations that cause client device 102 totransmit modification message 450 across network 120 to computing system130, e.g., via communications interface 118 using any appropriatecommunications protocol.

In some instances, API 428, may receive and route modification message450 to executed interface provisioning module 430, which may performoperations (not illustrated in FIG. 4E) that parse modification message450 and extract the unique identifiers of user 101, client device 102,or executed chatbot application 108, and perform operations thatauthenticate an identity of user 101 or client device 102 (e.g., basedon portions of the unique identifiers of user 101 or client device 102)or verify an authenticity of confirmation message 426 (e.g., based onthe unique identifier of executed chatbot application 108, such as anapplication cryptogram).

If executed interface provisioning module 430 were unable toauthenticate the identity of user 101 or client device 102, or to verifythe authenticity of confirmation message 426, executed interfaceprovisioning module 430 may generate an error message indicative of thefailed authentication or verification, which computing system 130 maytransmit back across network 120 to client device 102. Further, executedinterface provisioning module 430 may perform operations that discardreceived modification message 450, as described herein.

In other instances, and in response to a successful authentication ofthe identity of user 101 or client device 102, and/or a successfulverification of the authenticity of confirmation message 426, executedinterface provisioning module 430 may parse modification message 450 toextract modification flag 448 and additional data 446, which specifiesthe modification to candidate input value 336B (e.g., the correct streetaddress of “224 Park Street”). Executed interface provisioning module430 may process modification flag 448, which establishes the requestedmodification to candidate input value 336B by user 101, and performoperations that generate, for the second interface element of therequested digital interface, an element 432B of populated interface data432 that includes additional data 446 and reflects the modification tocandidate input value 336B. As illustrated in FIG. 4E, executedinterface provisioning module 430 may store element 432B of populatedinterface data 432 within a portion of interface data store 138, andassociate element 432B with interface identifiers 330 of the requesteddigital interface and with corresponding elements of layout data 332 andmetadata 334 associated with the requested digital interface (e.g.,layout data element 332B and metadata element 334B).

Although not illustrated in FIGS. 4A-4E, computing system 130 may, inconjunction with client device 102, perform any of the exemplaryprocesses described herein to (i) verify an accuracy of each of thecandidate input values associated with the interface elements of therequested digital interface based on sequential and successive elementsof message data programmatically exchanged with executed chatbotapplication 108 during the existing chatbot session, and (ii) populatethe interface elements of the requested digital interface (e.g., thedigital interface associated with the credit card application) based oncorresponding ones of the verified input values. For example, and uponcompletion of these exemplary processes, executed chatbot engine 142 maystore, within interface data store 138, elements of populated interfacedata 432 that specify the verified (e.g., confirmed or modified) inputvalue for each of the interface elements included within the requesteddigital interface, e.g., the digital interface for the credit cardapplication.

Through the implementation of these exemplary processes, computingsystem 130 may populate fully the requested digital interface based onelements of messaging data exchanged programmatically within theestablished chatbot session. By populating the requested digitalinterface without requiring the rendering and presentation of theinterface elements by client device 102, certain of these exemplaryprocesses may enhance an ability of a user to interact with thesecomplex digital interfaces through devices having display units or inputunits of limited functionality, such as smart phones, wearable devices,and digital assistants. Further, and based on additional message dataexchanged programmatically through the chatbot session, certain of theseexemplary processes may initiate a performance of additional operationsassociated with the populated interface data without rendering thedigital interface for presentation by client device 102.

By way of example, and upon population of the requested digitalinterface based on the elements of messaging data exchangedprogrammatically within the established chatbot session, executedchatbot engine 142 may generate a confirmatory message that includes anadditional flag indicative of the completed population of the requesteddigital interface. The confirmatory message may, in some instances, alsoinclude additional textual data that, when presented within a portion ofchatbot interface 200 by client device 102, prompts user 101 to providefurther input to client device 102 that either requests a submission ofthe credit card application for review and processing (e.g., based aconcatenation of the elements of populated interface data 432, asmaintained within interface data store 138), or alternatively, requestsan opportunity to review the requested digital interface prior tosubmission. Executed chatbot engine 142 may also package data associatedwith, or identifying, the established and ongoing chatbot session intothe confirmatory message, such as a session identifier or a cryptogramassociated with chatbot engine 142.

Executed chatbot engine 142 may perform additional operations that causecomputing system 130 to transmit the confirmatory message across network120 to client device 102, e.g., via the corresponding communicationsinterface using any appropriate communications protocol. In someinstances, a programmatic interface established and maintained by clientdevice 102, such as API 408 of client device 102, may receive and routethe confirmatory message to executed chatbot application 108. Further,and based on portions of the confirmatory message (e.g., the additionalflag, information identifying the established and ongoing chatbotsession, such as the session identifier or cryptogram, etc.), executedchatbot application 108 may determine that the confirmatory messagerepresents a new message within the established and ongoing chatbotsession.

Executed chatbot application 108 may parse the confirmatory message toextract the additional textual data, and may route the additionaltextual data to display unit 116A for presentation within acorresponding portion of chatbot interface 200. Referring to FIG. 5 ,and when presented within chatbot session area 202 of chatbot interface200, textual content 502 may confirm, to user 101, the successfulpopulation of the requested digital interface (e.g., the digitalinterface associated with the credit card application), and may promptuser 101 to provide additional input to client device 102 that eitherrequests a submission of the credit card application for review andprocessing, or alternatively, requests an opportunity to review therequested digital interface prior to submission.

For example, user 101 may elect to request submission of the credit cardapplication for review and processing, and as illustrated in FIG. 5 ,may provide input to client device 102 that confirms the requestedsubmission, e.g., by establishing contact between the finger or thestylus and the portion of the surface of the pressure-sensitive,touchscreen display unit that corresponds to a confirmation andsubmission icon 504 within chatbot interface 200. Based on theprovisioned input, executed chatbot application 108 may perform any ofthe exemplary processes described herein to generate and transmit asubmission request across network 120 to computing system 130, e.g., viacommunications interface 118 using any appropriate communicationsprotocol. A secure, programmatic interface established and maintained bycomputing system 130, such as API 428, may receive and route thesubmission request to executed chatbot engine 142, which may performoperations that concatenate the elements of populated interface data 432(e.g., as maintained within interface data store 138) to establishcredit-card application data, and that transmit the credit-cardapplication data to one or more additional computing systems for review,processing, and approval.

In other examples, user 101 may elect to review the populated digitalinterface for the credit card application prior to review andprocessing. As illustrated in FIG. 5 , may provide input to clientdevice 102 that confirms the request to review the populated digitalinterface, e.g., by establishing contact between the finger or thestylus and the portion of the surface of the pressure-sensitive,touchscreen display unit that corresponds to review application icon 506within chatbot interface 200. For example, review application icon 506may represent a deep-link to the populated digital interface associatedwith the credit card application, and based on the provisioned input,executed chatbot application 108 may perform operations that causeclient device 102 to render and present the populated digital interfacevia display unit 116A, e.g., within a viewing window of a web browserexecuted by client device 102.

In other instances, and based on the provisioned input, executed chatbotapplication 108 may perform any of the exemplary processes describedherein to generate and transmit an application review request acrossnetwork 120 to computing system 130, e.g., via communications interface118 using any appropriate communications protocol. A secure,programmatic interface established and maintained by computing system130, such as API 428, may receive and route the application reviewrequest to executed chatbot engine 142, which may perform operationsthat concatenate the elements of populated interface data 432 (e.g., asmaintained within interface data store 138) to establish credit-cardapplication data, and that transmit the credit-card application data toclient device 102, e.g., for rendering and presentation on display unit116A (such as within the viewing window of the executed web browser), orin a predetermined format (e.g., a PDF document) to an email address ofuser 101.

In some examples, described herein, user 101 may provide input to clientdevice 102, and as such, may interact with chatbot interface 200, via aminiaturized “virtual” keyboard presented within digital chatbotinterface 200. In other instances, chatbot application 108 may alsoinclude one or more executed text-to-speech module that, when executedby client device 102, convert elements of the programmatically exchangedmessage data received from executed chatbot engine 142 (e.g., responsemessage data 406 of FIG. 4A, response message data 436 of FIG. 4C, etc.)into corresponding elements of audio content for presentation to user101 via a corresponding speaker, e.g., an embedded speaker coupled toprocessor 104 or a remote speaker coupled to client device 102 via oneor more communications protocols, such as a Bluetooth™ or a NFCcommunication protocol.

In other examples, any of the exemplary elements of user input (e.g.,input 417 of FIG. 4C, input 443 of FIG. 4E, etc.) may include audiocontent representative of a spoken utterance, which may be captured by acorresponding microphone embedded into client device 102 (e.g., as aportion of input unit 116B) or in communication with client device 102(e.g., across a short-range communications channel, such as Bluetooth™,etc.). Executed chatbot application 108 may include one or moreapplication modules that convert the audio content into correspondingelements of the input data described herein, input data 418 of FIG. 4C,input data 444 of FIG. 4E, etc. As such, these exemplary processes, asdescribed herein, may enhance an ability of a user to interact withthese complex digital interfaces during a programmatically establishedchatbot session through devices having display units of limitedfunctionality, such as wearable devices or smart watches.

FIG. 6 is a flowchart of a process 600 for dynamically configuring,populating a digital interface based on sequential elements of messagedata exchanged during a programmatically established chatbot session, inaccordance with some exemplary embodiments. For example, anetwork-connected computing system operating within environment 100,such as computing system 130, may perform one or more of the steps ofexemplary process 600.

Referring to FIG. 6 , computing system 130 may receive one or moreelements of chatbot session data from client device 102 (e.g., in step602). As described herein, the chatbot session data may be generated byone or more chatbot application programs executed at client device 102(e.g., chatbot application 108 of FIG. 1 ) during a chatbot sessionestablished between the one or more chatbot application programs and achatbot engine executed at computing system 130 (e.g., chatbot engine142 of FIG. 1 ). In some instances, the session data may include messagedata, which includes textual content representative of a messageprovided by user 101 as an input to a chatbot interface generated andrendered for presentation by the one or more executed chatbotapplication. Further, and as described herein, the message may requestaccess to one or more digital interfaces available to, and supported by,computing system 130, such as, but not limited to, a digital interfaceassociated with a credit card application, an application for amortgage, or a tax return.

Computing system 130 may store the received session data within aportion of a data repository associated with the established chatbotsession (e.g., in step 604). Further, computing system 130 may performany of the exemplary processes described herein to extract the messagedata from the received session data, and may apply any of the exemplarynatural language processing (NLP) algorithms described herein to all ora portion of the message data (e.g., in step 606). Based on theapplication of these exemplary NLP algorithms to all or the portion ofthe message data, computing system 130 may perform any of the exemplaryprocesses described herein to generate linguistic element data, whichincludes each discrete linguistic element within the message, andcontextual information that specifies a context or meaning of thecombination of the discrete linguistic elements (e.g., also in step606).

Based on the linguistic element data and the contextual information,computing system 130 may perform any of the exemplary processesdescribed herein to identify the digital interface requested by message(e.g., in step 608). Computing system may also perform any of theexemplary processes described herein to obtain, for the requesteddigital interface, layout data that identifies one or more discreteinterface elements within the requested digital interface and thatspecifies a sequential position of the discrete interface elementswithin the requested digital interface, and corresponding elements ofinformation, e.g., metadata, that characterize a type or range of inputdata associated with each of the discrete interface elements (e.g., instep 610).

Based on portions of the obtained layout data and metadata associatedwith the requested digital interface, computing system 130 may performany of the exemplary processes described herein to compute a candidateinput value for each interface element disposed a correspondingsequential position within the requested digital interface (e.g., instep 612). In some instances, computing system 130 may maintain at leastone of the candidate input values within a locally accessible datarepository (e.g., within one of user database 132, confidential datastore 134, or chatbot session data store 136 of FIG. 1 ), and computingsystem 130 may perform operations that identify and extract the at leastone of the candidate input values from the locally accessible datarepositories based on corresponding elements of the layout data andmetadata.

In other instances, in step 612, computing system 130 may compute atleast one of the candidate input values based on an application of anyof the exemplary predictive models described herein to model inputassociated with corresponding ones of the interface elements within therequested digital interface. By way of example, and for a particular oneof the interface elements, the model input may include, but is notlimited to, all or a selected portion of the elements of metadataassociated with the particular interface element (e.g., thatcharacterizes the type, range, or format of the appropriate input dataassociated with the particular interface element), additional elementsof profile data, confidential data, or chatbot session data associatedwith user 101 (e.g., as extracted from, or selectively derived from datamaintained within, one or more of user database 132, confidential datastore 134, or chatbot session data store 136), and/or further elementsof profile data, confidential data, or chatbot session data associatedwith additional users of computing system 130 that are demographicallysimilar to user 101. As described herein, examples of the predictivemodels include, a deterministic or stochastic statistical process, amachine learning processes, or an artificial intelligence model.

In step 614, computing system 130 may perform any of the exemplaryprocesses described herein to select an element of the layout data, anelement of the metadata, and the candidate input value associated with acorresponding one of the interface elements disposed at a firstsequential position within the requested digital interface, e.g., a“first” interface element. Based on the layout data element, themetadata element, and the candidate input value associated with thefirst interface element, computing system 130 may perform any of theexemplary processes described herein to generate message data that, whenexchanged programmatically with client device 102 during the establishedchatbot session, enables user 101 to interact with the first interfaceelement of the requested digital interface by, among other things,confirming an accuracy of the candidate input value associated with thefirst interface element (e.g., in step 616). As described herein, thegenerated message data may include the candidate input value associatedwith the first interface element, and may also include programmaticallygenerated textual data that prompts user 101 to confirm the accuracy ofthe candidate input value. Further, in step 616, computing system 130may also transmit the generated message data across network 120 toclient device, e.g., during the established chatbot session.

As described herein, a secure programmatic interface of client device102 may receive and route the message data to an executed chatbotapplication, such as chatbot application 108 of FIG. 1 . The executedchatbot application may parse the received message data to extract thetextual data and the candidate input value associated with the firstinterface element, and may perform any of the exemplary processesdescribed herein to present the textual data and the candidate inputvalue within a corresponding portion of a presented chatbot interface,e.g., chatbot interface 200 described herein.

By way of example, and as described herein, user 101 may provideadditional input to chatbot interface 200 (e.g., via input unit 116B ofclient device 102) that either confirms a determined accuracy of thecandidate input value, or alternatively, requests a modification to thecandidate input value, e.g., based on a detected error or omission. Insome instances, the executed chatbot application may perform any of theexemplary processes described herein generate one or more elements ofmessage data that reflect the now-confirmed candidate input value or therequested modification to that candidate input value, and client device102 may transmit the one or more elements of message data across network120 to computing system 130, e.g., via communications interface 118using any appropriate communications protocol.

Computing system 130 may receive the one or more elements of responsemessage data from client device 102 (e.g., in step 618). In someinstances, computing system 130 may perform any of the exemplaryprocesses described herein to generate an element of populated interfacedata for the first interface element that includes the now-confirmedcandidate input value or alternatively, the requested modification tothat candidate input value, and to store the generated element ofpopulated interface data 432 within a portion of a locally accessibledata repository, such as interface data store 138 of FIG. 1 (e.g., instep 620).

In step 622, computing system 130 may perform any of the exemplaryprocesses described herein to select an element of the layout data, anelement of the metadata, and the candidate input value associated withan additional one of the interface elements disposed at a nextsequential position within the requested digital interface. Theadditional interface element may, as described herein, correspond to asecond interface element disposed subsequent to the first interfaceelement within the digital interface, or one or more further interfaceelements disposed subsequent to that second interface element. Based onthe layout data element, the metadata element, and the candidate inputvalue associated with the additional interface element, computing system130 may perform any of the exemplary processes described herein togenerate message data that, when exchanged programmatically with clientdevice 102 during the established chatbot session, enables user 101 tointeract with the additional interface element of the requested digitalinterface by, among other things, confirming an accuracy of thecandidate input value associated with the additional interface element,or requesting a modification to that candidate input value (e.g., instep 624). Further, in step 624, computing system 130 may also transmitthe generated message data across network 120 to client device, e.g.,during the established chatbot session.

In some instances, the secure programmatic interface of client device102 may receive and route the message data to the executed chatbotapplication, which may parse the received message data to extract thetextual data and the candidate input value associated with theadditional interface element. The executed chatbot application may alsocause client device 102 to perform any of the exemplary processesdescribed herein the present the textual data and the candidate inputvalue within a corresponding portion of a presented chatbot interface,e.g., chatbot interface 200 described herein.

Further, and as described herein, user 101 may provide additional inputto chatbot interface 200 (e.g., via input unit 116B of client device102) that either confirms a determined accuracy of the candidate inputvalue associated with the additional interface element, oralternatively, requests a modification to the candidate input valueassociated with the additional interface element. In some instances, theexecuted chatbot application may perform any of the exemplary processesdescribed herein generate one or more additional elements of responsemessage data that reflect the now-confirmed candidate input value or therequested modification to that candidate input value, and client device102 may transmit the one or more elements of response message dataacross network 120 to computing system 130, e.g., via communicationsinterface 118 using any appropriate communications protocol.

Computing system 130 may receive the one or more additional elements ofresponse message data from client device 102 (e.g., in step 626).Computing system 130 may perform any of the exemplary processesdescribed herein to generate an element of populated interface data forthe additional interface element that includes the now-confirmedcandidate input value or alternatively, the requested modification tothat candidate input value, and to store the generated element ofpre-populated interface data within a portion of the locally accessibledata repository, such as interface data store 138 of FIG. 1 (e.g., instep 628).

In some instances, computing system 130 may parse the layout dataassociated with the requested digital interface, and may perform any ofthe exemplary processes described to determine whether the locallyaccessible data repository maintains an element of populated interfacedata for each of the interface elements disposed sequentially within therequested digital interface (e.g., in step 630). If, for example,computing system 130 were to determine that one or more of the interfaceelements disposed sequentially within the requested digital interfaceawait processing and population (e.g., step 630; YES), exemplary process600 may pass back to step 622, and computing system 130 may perform anyof the exemplary processes described herein to obtain an element of thelayout data, an element of the metadata, and the candidate input valueassociated with an additional one of the interface elements disposed ata next sequential position within the requested digital interface.

Alternatively, if computing system 130 were to determine the locallyaccessible data repository maintains an element of populated interfacedata for each of the interface elements disposed sequentially within therequested digital interface (e.g., step 630; NO), computing system 130may perform any of the exemplary processes described herein to generatea confirmatory message indicative of the completed population of therequested digital interface, and to transmit that confirmatory messageacross network 120 (e.g., in step 632). In some instances, computingsystem 130 may receive a response to the confirmatory message fromclient device 102 (e.g., as generated programmatically based onadditional user input during the existing chatbot session), and mayperform one or more operations involving the elements of the populatedinterface data in accordance with the received response (e.g., in step634). Examples of these operations include, but are not limited to,concatenating the elements of the pre-populated interface data toestablish credit-card application data, transmitting the credit-cardapplication data to one or more additional computing systems for review,processing, and approval, or transmitting formatted or unformattedportions of the credit-card application data to client device 102.Exemplary process 600 is then complete in step 636.

III. Exemplary Hardware and Software Implementations

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Exemplary embodiments of the subject matterdescribed in this specification, such as, but not limited to, chatbotapplication 108, chatbot engine 142, natural-language processing (NLP)engine 144, predictive engine 146, APIs 301, 408, and 428, sessionmanagement module 320, interface selection module 326, messagegeneration module 402, processing module 410, triggering module 420,messaging module 424, and interface provisioning module 430, can beimplemented as one or more computer programs, i.e., one or more modulesof computer program instructions encoded on a tangible non-transitoryprogram carrier for execution by, or to control the operation of, a dataprocessing apparatus (or a computer system).

Additionally, or alternatively, the program instructions can be encodedon an artificially generated propagated signal, such as amachine-generated electrical, optical, or electromagnetic signal that isgenerated to encode information for transmission to suitable receiverapparatus for execution by a data processing apparatus. The computerstorage medium can be a machine-readable storage device, amachine-readable storage substrate, a random or serial access memorydevice, or a combination of one or more of them.

The terms “apparatus,” “device,” and “system” refer to data processinghardware and encompass all kinds of apparatus, devices, and machines forprocessing data, including, by way of example, a programmable processorsuch as a graphical processing unit (GPU) or central processing unit(CPU), a computer, or multiple processors or computers. The apparatus,device, or system can also be or further include special purpose logiccircuitry, such as an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit). The apparatus, device, orsystem can optionally include, in addition to hardware, code thatcreates an execution environment for computer programs, such as codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program, which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data, such as one ormore scripts stored in a markup language document, in a single filededicated to the program in question, or in multiple coordinated files,such as files that store one or more modules, sub-programs, or portionsof code. A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, such as an FPGA (field programmable gate array), an ASIC(application-specific integrated circuit), one or more processors, orany other suitable logic.

Computers suitable for the execution of a computer program include, byway of example, general or special purpose microprocessors or both, orany other kind of central processing unit. Generally, a CPU will receiveinstructions and data from a read-only memory or a random-access memoryor both. The essential elements of a computer are a central processingunit for performing or executing instructions and one or more memorydevices for storing instructions and data. Generally, a computer willalso include, or be operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,such as magnetic, magneto-optical disks, or optical disks. However, acomputer need not have such devices. Moreover, a computer can beembedded in another device, such as a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storagedevice, such as a universal serial bus (USB) flash drive, to name just afew.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, such as EPROM, EEPROM, and flash memory devices; magneticdisks, such as internal hard disks or removable disks; magneto-opticaldisks; and CD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display unit, such as a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, such as a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, suchas visual feedback, auditory feedback, or tactile feedback; and inputfrom the user can be received in any form, including acoustic, speech,or tactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser’s device in response to requests received from the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, such as a data server, or that includes a middlewarecomponent, such as an application server, or that includes a front-endcomponent, such as a computer having a graphical user interface or a Webbrowser through which a user can interact with an implementation of thesubject matter described in this specification, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, such as a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), such as the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data, such as an HTML page, to auser device, such as for purposes of displaying data to and receivinguser input from a user interacting with the user device, which acts as aclient. Data generated at the user device, such as a result of the userinteraction, can be received from the user device at the server.

While this specification includes many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments may also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment mayalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination may in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

Various embodiments have been described herein with reference to theaccompanying drawings. It will, however, be evident that variousmodifications and changes may be made thereto, and additionalembodiments may be implemented, without departing from the broader scopeof the disclosed embodiments as set forth in the claims that follow.

Further, other embodiments will be apparent to those skilled in the artfrom consideration of the specification and practice of one or moreembodiments of the present disclosure. It is intended, therefore, thatthis disclosure and the examples herein be considered as exemplary only,with a true scope and spirit of the disclosed embodiments beingindicated by the following listing of exemplary claims.

What is claimed is: 1-20. (canceled)
 21. An apparatus, comprising: acommunications interface; a memory storing instructions; and at leastone processor coupled to the communications interface and to the memory,the at least one processor being configured to execute the instructionsto: receive, via the communications interface, first messaging data froma device during a communications session, the first messaging data beingassociated with a request to access a first digital interface; based onan application of a trained machine-learning or artificial-intelligenceprocess to an input dataset that includes information associated with afirst interface element of the first digital interface, generate textualcontent that characterizes at least one of the first interface elementor a candidate input value for the first interface element; andtransmit, via the communications interface, second messaging data to thedevice during the communications session, the second messaging datacomprising the candidate input value and the textual content, and thedevice being configured to present the textual content and the candidateinput value within a second digital interface associated with thecommunications session.
 22. The apparatus of claim 21, wherein the atleast one processor is further configured to executed the instructionsto determine the candidate input value for the first interface elementof the first digital interface.
 23. The apparatus of claim 21, wherein:the at least one processor is further configured to execute theinstructions to establish the communications session with an applicationprogram executed at the device; the executed application programgenerates the first messaging data; and the second messaging data causesthe executed application program to present the textual content and thecandidate input value within the second digital interface.
 24. Theapparatus of claim 23, wherein: the at least one processor is furtherconfigured to execute the instructions to: receive, via thecommunications interface, third messaging data from the device duringthe communications session, the third messaging data comprising aconfirmation of the candidate input value, and the third messaging databeing generated by the executed application program; and based on thethird messaging data, generate populated interface data that associatesthe first interface element with the confirmed candidate input value,and store the populated interface data within a portion of the memory.25. The apparatus of claim 23, wherein the at least one processor isfurther configured to execute the instructions to: receive, via thecommunications interface, third messaging data from the device duringthe communications session, the third messaging data comprising amodification to the candidate input value, and the third messaging databeing generated by the executed application program; modify thecandidate input value based in accordance with the third messaging data;and generate populated interface data that associates the firstinterface element with the modified candidate input value, and store thepopulated interface data within a portion of the memory.
 26. Theapparatus of claim 21, wherein the textual content comprises one or morelinguistic elements that characterize at least one of the firstinterface element or the candidate input value.
 27. The apparatus ofclaim 21, wherein the at least one processor is further configured toexecute the instructions to: obtain one or more linguistic elements fromthe first messaging data, and based on an application of an additionaltrained machine-learning process or an additionalartificial-intelligence process to at least a portion of the firstmessaging data, generate contextual information associated with thefirst messaging data; and perform operations that identify the firstdigital interface based on at least one of the contextual information orthe one or more linguistic elements.
 28. The apparatus of claim 21,wherein: the first digital interface comprises a plurality of interfaceelements, the plurality of interface elements comprising the firstinterface element; and the information associated with the firstinterface element comprises an element of metadata associated with thefirst interface element.
 29. The apparatus of claim 28, wherein the atleast one processor is further configured to obtain the element ofmetadata, the element of metadata characterizing an input data typeassociated with the first interface element.
 30. The apparatus of claim21, wherein the at least one processor is further configured to executethe instructions to load, from the memory, layout data and metadataassociated with the first digital interface, the layout data comprisingindexing information that specifies sequential positions of theplurality of interface elements within the first digital interface, andthe metadata identifying input data types for the plurality of interfaceelements.
 31. The apparatus of claim 30, wherein the at least oneprocessor is further configured to executed the instructions to:identify the first interface element based on the indexing information,the first interface element being disposed within the first digitalinterface at a first one of the sequential positions; obtain a firstelement of the layout data associated with the first interface element,and obtain a first element of the metadata associated with the firstinterface element; and determine the candidate input value for the firstinterface element based on at least one of the first layout data elementor the first metadata element.
 32. A computer-implemented method,comprising: receiving, using at least one processor, first messagingdata from a device during a communications session, the first messagingdata being associated with a request to access a first digitalinterface; based on an application of a trained machine-learning orartificial-intelligence process to an input dataset that includesinformation associated with a first interface element of the firstdigital interface, generating, using the at least one processor, textualcontent that characterizes at least one of the first interface elementor a candidate input value for the first interface element; andtransmitting, using the at least one processor, second messaging data tothe device during the communications session, the second messaging datacomprising the candidate input value and the textual content, and thedevice being configured to present the textual content and the candidateinput value within a second digital interface associated with thecommunications session.
 33. The computer-implemented method of claim 32,further comprising determining, using the at least one processor, thecandidate input value for the first interface element of the firstdigital interface.
 34. The computer-implemented method of claim 32,wherein: the computer-implemented method further comprises, using the atleast one processor, establishing the communications session with anapplication program executed at the device; the executed applicationprogram generates the first messaging data; and the second messagingdata causes the executed application program to present the textualcontent and the candidate input value within the second digitalinterface.
 35. The computer-implemented method of claim 34, furthercomprising: receiving, using the at least one processor, third messagingdata from the device during the communications session, the thirdmessaging data comprising a confirmation of the candidate input value,and the third messaging data being generated by the executed applicationprogram; and based on the third messaging data, generating, using the atleast one processor, populated interface data that associates the firstinterface element with the confirmed candidate input value, and storingthe populated interface data within a portion of a data repository usingthe at least one processor.
 36. The computer-implemented method of claim34, further comprising: receiving, using the at least one processor,third messaging data from the device during the communications session,the third messaging data comprising a modification to the candidateinput value, and the third messaging data being generated by theexecuted application program; modifying, using the at least oneprocessor, the candidate input value based in accordance with the thirdmessaging data; and generating, using the at least one processor,populated interface data that associates the first interface elementwith the modified candidate input value, and store the populatedinterface data within a portion of the memory.
 37. Thecomputer-implemented method of claim 32, wherein: the textual contentcomprises one or more linguistic elements that characterize at least oneof the first interface element or the candidate input value; and thecomputer-implemented method further comprises: obtaining, using the atleast one processor, one or more additional linguistic elements from thefirst messaging data, and based on an application of an additionaltrained machine-learning process or an additionalartificial-intelligence process to at least a portion of the firstmessaging data, generate contextual information associated with thefirst messaging data; and performing operations, using the at least oneprocessor, that identify the first digital interface based on at leastone of the contextual information or the one or more additionallinguistic elements.
 38. The computer-implemented method of claim 32,wherein: the first digital interface comprises a plurality of interfaceelements, the plurality of interface elements comprising the firstinterface element; and the information associated with the firstinterface element comprises an element of metadata associated with thefirst interface element, the element of metadata characterizing inputdata type associated with the first interface element.
 39. Thecomputer-implemented method of claim 32, further comprising: using theat least one processor, loading, from a data repository, layout data andmetadata associated with the first digital interface, the layout datacomprising indexing information that specifies sequential positions ofthe plurality of interface elements within the first digital interface,and the metadata identifying input data types for the plurality ofinterface elements; identifying, using the at least one processor, thefirst interface element based on the indexing information, the firstinterface element being disposed within the first digital interface at afirst one of the sequential positions; obtaining, using the at least oneprocessor, a first element of the layout data associated with the firstinterface element, and obtain a first element of the metadata associatedwith the first interface element; and determining, using the at leastone processor, the candidate input value for the first interface elementbased on at least one of the first layout data element or the firstmetadata element.
 40. A tangible, non-transitory computer-readablemedium storing instructions that, when executed by at least oneprocessor, cause the at least one processor to perform a method,comprising: Receiving first messaging data from a device during acommunications session, the first messaging data being associated with arequest to access a first digital interface; based on an application ofa trained machine-learning or artificial-intelligence process to aninput dataset that includes information associated with a firstinterface element of the first digital interface, generating textualcontent that characterizes at least one of the first interface elementor a candidate input value for the first interface element; andtransmitting second messaging data to the device during thecommunications session, the second messaging data comprising thecandidate input value and the textual content, and the device beingconfigured to present the textual content and the candidate input valuewithin a second digital interface associated with the communicationssession.